Computer-aided localization of site of origin of cardiac activation with discriminator leads

ABSTRACT

A method for quantifying, during pacemapping, a comparison of a BSPM of interest to a pace site BSPM. The method may include receiving at a computing device a plurality of ECG signals from an acquisition system. The pace site BSPM may be calculated using the plurality of ECG signals. The BSPM of interest may be compared to the pace site BSPM, by: retrieving the BSPM of interest from memory accessible by the computing device; and, calculating one or more comparison metrics for the BSPM of interest as compared to the pace site BSPM. An indication of similarity between the BSPM of interest and the pace site BSPM based on the comparison metric calculated may be displayed on a user interface in communication with the computing device.

FIELD OF THE INVENTION

The present invention relates to computer-aided localization of site oforigin of cardiac activation for catheter ablation.

BACKGROUND OF THE INVENTION

Ventricular tachycardia (VT) is one of the most difficult managementchallenges in clinical cardiac electrophysiology. The spectrum ofventricular arrhythmias spans a wide range of clinical presentationsthat include premature ventricular complexes (PVCs), non-sustainedventricular tachycardia (NSVT), sustained ventricular tachycardia (VT)and ventricular fibrillation (VF). Any of these presentations can occurin patients with or without structural heart disease. This spectrumapplies to any source of tachycardia originating below the His bundlewhether from the bundle branches, Purkinje fibers or ventricularmyocardium.

VT most commonly occurs in the setting of structural heart disease, suchas coronary artery disease, heart failure, cardiomyopathy, congenitalheart disease or following cardiac surgery. Prior myocardial infarction(MI) is by far the most common cause of sustained VT. Ventriculartachyarrhythmia associated with MI occurs in two stages. During theacute phase of MI, polymorphic ventricular tachycardia that candegenerate into ventricular fibrillation is most common. On the otherhand, sustained monomorphic VT generally arises from the anatomicsubstrate of a healed MT that usually develops within 2 weeks after anMI and remains indefinitely. This substrate of healthy and damagedmyocardium interlaced with fibrous tissue is found primarily at theborder zone of the scar. Fibrosis creates areas of conduction block andincreases the separation of myocyte bundles, slowing conduction throughmyocyte pathways in the border zone of the infarct thus creating asubstrate that supports re-entry when an appropriate trigger occurs.With present management of MI, the incidence of sustainedpost-infarction VT is low, and fewer than 5% of acute MI survivors haveinducible ventricular tachycardia when studied early after the acuteevent. VT exits the scar into the healthy myocardium and depolarizes themyocardium sequentially from this exit site. The location of the exit isresponsible for the morphology of the ECG signal.

Sustained monomorphic VT occurring in the absence of structural orelectrical heart disease is called idiopathic VT. Idiopathic VT canarise from different sites, but the right ventricular outflow tract(most commonly within 1-2 cm of the pulmonary valve) is by far the mostcommon and accounts for approximately 10% of VTs seen by specializedarrhythmia services. Other potential sites include the left ventricularoutflow tract, aortic sinuses of Valsalva (most commonly left) from acrescent of ventricular epicardium underlying the base of the sinus atthe aortoventricular junction, in the endocardium adjacent to the mitralannulus and finally from the left ventricular epicardium remote from thesinuses of Valsalva, at sites adjacent to the coronary vasculature.These idiopathic VTs usually have a focal origin caused by triggeredactivity or abnormal automaticity.

Suppression of VT may be accomplished with the use of implantablecardioverter-defibrillators (ICDs), anti-arrhythmic drugs, arrhythmiasurgery and catheter ablation. While antiarrhythmic drugs are consideredfirst line therapy and are commonly used to complement therapy, they arenot completely effective in preventing VT episodes and may causesignificant cardiac and non-cardiac side-effects. ICD is the onlytreatment modality that has been demonstrated to offer a significantreduction in mortality in patients with scar-related VT. Althoughimplantable cardioverter-defibrillators (ICDs) can improve the prognosisfor patients with VT, recurrent VT can still be life-threatening.Catheter ablation offers a curative treatment for certain types ofidiopathic VT and has been suggested to have a benefit for patients whohave suffered prior MI in many case studies.

Cardiac mapping refers to all procedures involving recording ofbody-surface electrocardiograms or endocardial/epicardial electrogramsgenerated due to the spread of the cardiac action potential. This can berecorded from the body surface using either conventional 12-leadelectrocardiogram (ECG) or multiple leads (such as for body surfacepotential mapping (BSPM)), the endocardium or the epicardium. Cardiacmapping provides a means of visualizing the pathophysiologicalmechanisms underlying ventricular tachycardia, which is crucial fordirecting catheter ablation procedures.

Several conventional and advanced mapping techniques are frequentlyutilized to accomplish a successful catheter ablation. However, many ofthese mapping techniques are hampered by either hemodynamic instabilityor non-sustained nature of some tachycardias.

Conventional endocardial mapping techniques with catheters placedpercutaneously into the heart chambers continue to be the most popularcardiac mapping modality. These catheters are localized and navigatedusing fluoroscopy. Several conventional mapping techniques have beendeveloped over the last few decades to help understanding the mechanismsof arrhythmias and to guide catheter ablation. These conventionalmapping techniques include activation mapping, pacemapping andentrainment mapping.

Pacemapping is a commonly used tool for mapping non-sustained orhemodynamically unstable VT; it is based upon the principle thatactivation of the heart from a given site will yield a reproducible bodysurface electrocardiogram (ECG) morphology and that pacing from a sitevery close to the site at which VT activates the heart (i.e. the site oforigin) will result in a matching ECG morphology. However, thistechnique has some limitations. Comparison of the 12-lead ECG morphologybetween a pace-map and clinical tachycardia is frequently completelysubjective or semi-quantitative. Discrepancies in ablation results mayresult, in part, from subjective differences in the opinion of apace-map match to the clinical tachycardia. Another important limitationis that increasing the strength to pace diseased tissues, as inscar-related VT, can excite tissues more distant to the area ofstimulation (even if unipolar pacing is used) which may lead to a 12/12match even 1-1.5 cm away from successful ablation sites. This techniqueis therefore very time consuming and is limited by imperfect accuracyand spatial resolution, subjectivity of interpretation, and by the needfor an intuitive interpretation of the ECG to direct cathetermanipulation.

BSPM incorporates data from a much larger number of electrodes, butremains limited by the remote location of the recording site from thecardiac surface resulting in poor spatial resolution of electricalevents. The recent development of electrocardiographic imaging (ECGI)represents a further refinement of this technique, combining BSPM andheart torso geometric information to produce detailed electroanatomicalmaps of the epicardial surface through application of inverse solutionmathematical algorithms. This methodology has permitted accuratelocalization of focal activation sources, as well as detailed activationsequences during re-entrant arrhythmias. ECGI was recently used toassist in the diagnosis and guiding catheter ablation of focalidiopathic as well as scar related VT. A number of limitations are stillunder investigation, the most important being the accuracy, but also thecomplexity of the procedure and the need for a long processing time fromelectrocardiographic signal acquisition to 3D display of the derivedepicardial potentials.

There is therefore a need for a system that assists in the localizationof the site of origin.

BRIEF DESCRIPTION OF THE DRAWINGS

In drawings which illustrate by way of example only a preferredembodiment of the invention,

FIG. 1 is a schematic diagram of a computer-aided localization system.

FIG. 2 is a schematic diagram of an arrangement of 120 leads on thetorso for body surface potential mapping. The left half of the gridrepresents anterior surface and right half represents the posteriorsurface of the chest. Transverse levels (labeled 1′, 2 . . . 0.10′) are1-inch apart from neck to waist; potentials at 352 nodes (unlabelledsolid squares) are interpolated from potentials recorded at 120 sites(numbered circles); squares labelled ‘g’ indicate sites of precordialleads V1-V6; squares labelled ‘y’ mark sites where electrodes ofMason-Likar substitution for extremity leads are placed; squareslabelled ‘r’ are sites of the EASI leads.

FIG. 3 is an illustration of a 17-segment model of the left ventricle ona circumferential polar plot and the nomenclature for assignments of theCARTO points to anatomical locations. Diagram of vertical long-axis(VLA, 2-chamber view), horizontal long-axis (HLA, 4-chamber view), andshort-axis (SA) planes showing the name, location, and anatomiclandmarks for selection of the basal, mid-cavity, and apical short axisslices for the recommended 17-segment system.

FIG. 4 is a block diagram for identifying the segment of the heart inwhich the activation site of origin is likely located.

FIG. 5 is a block diagram for displaying an objective measure of thesimilarity between an BSPM of interest and a pace site BSPM.

FIG. 6 shows 12-lead QRS integral templates for 16 endocardial segments.ECG leads on the abscissa in each of the 16 panels are ordered inCabrera sequence (aVL, I, −aVR, II, aVF, III) followed by precordialleads (V1 to V6). Dark-grey (red) bars indicate positive QRS integralswhile light-grey (green) bars indicate negative QRS integrals.

FIG. 7 shows 12-lead 100 msec QRS integral templates for 16 endocardialsegments. ECG leads on the abscissa in each of the 16 panels are orderedin Cabrera sequence (aVL, I, −aVR, II, aVF, III) followed by precordialleads (V1 to V6). Dark-grey (red) bars indicate positive QRS integralswhile light-grey (green) bars indicate negative QRS integrals.

FIG. 8 shows recorded 120-lead QRS integral body surface potential maptemplates for 16 endocardial segments. The left side of each mapcorresponds to the front and the right side corresponds to the back ofthe chest. ECG leads V1-V6 are identified by the black dots. Areas 410and 400 of the maps represent positive and negative integrals,respectively, whereas the white line 420 marks the zero integral. Thelocations of the positive/negative extremes (maxima/minima) areindicated by white dots in areas 410 and 400, respectively, and theiramplitudes are expressed in mVms in the upper left and upper right ofthe map respectively. The isointegral lines are separated byautomatically determined linear incremental steps that depend on themagnitude of the maxima and the minima and is, for display clarity,restricted to an upper limit of 15 contour lines per map.

FIG. 9 shows recorded 120-lead 100 msec QRS integral body surfacepotential map templates for 16 endocardial segments. Note the similarmorphology to the QRS BSPM templates with lower amplitudes of the maximaand minima.

FIG. 10 shows predicted QRS integral BSPM templates from 12-leads ECG byusing regression coefficients for 16 endocardial segments. Note thesimilar morphology to the recorded BSPM and the difference in the valuesof the maxima and minima.

FIG. 11 shows predicted initial 100 msec QRS integral BSPM templatesfrom 12-lead ECG by using regression coefficients for 16 endocardialsegments. Note the similar morphology to the recorded BSPM and thedifference in the values of the maxima and minima.

FIG. 12 shows 12-lead 100 msec QRS integral templates for 16 endocardialsegments with application of initial discriminator leads II and V4. PartA indicates templates obtained selectively from those patterns thatfeature a positive QRS integral in leads II and V4. Part B indicatestemplates with a positive QRS integral in lead II and negative in leadV4. Part C indicates templates with a negative QRS integral in lead IIand positive in lead V4. Part D indicates templates with a negative QRSintegral in leads II and V4.

FIG. 13 depicts a bull's eye display of a left-ventricular endocardium.The three-dimensional endocardial surface is modelled on a realisticrepresentation of the human heart and is represented by 238 planartriangles within 16 anatomical segments and projected onto a polardisplay. There are six basal segments (1-6), six mid-segments (7-12),and 4 apical segments denoted as anterior (1 and 7), anteroseptal (2, 8,and 13), inferoseptal (3, 9, and 14), inferior (4 and 10), inferolateral(5, 11, and 15), and anterolateral (6, 12, and 16).

FIG. 14 indicates high-resolution localization of the activation site.Localization of left ventricular pacing site is illustrated in a16-segment model (top left and right) with probable activation siteidentified by arrow pointing to the white triangle (top-left) and circle(top right). The initial 120-ms interval of the QRS is identified(bottom left) on ECG tracings of 8 frontal-plane leads and 6 precordialleads of the standard 12-lead ECG. Location of the actual pacing site(white dot) as indicated using a CARTO™ electroanatomical map (bottomright) is also shown.

DETAILED DESCRIPTION OF THE INVENTION

In one embodiment, there is provided a method for localizing anactivation site of origin to a segment of the heart comprising:comparing a body surface potential map (BSPM) of interest to each BSPMtemplate of a pre-determined set of BSPM templates, the set comprisingone BSPM template for each pre-defined segment of the heart, wherein theBSPM of interest is calculated based on a plurality of simultaneouslyrecorded electrocardiographic (ECG) signals and wherein comparingcomprises, for each BSPM template of the set of BSPM templates:retrieving a BSPM template from memory; and calculating one or morecomparison metrics for the BSPM template retrieved as compared to theBSPM of interest, wherein each of the BSPM templates was generated byaveraging BSPMs of all pacing sites within the associated segment of theheart from a collection of BSPMs stored in memory; identifying the BSPMtemplate that most closely resembles the BSPM of interest using thecomparison metrics calculated in order to identify the segment of theheart in which the activation site of origin is likely to be located.

In further aspects of this embodiment, each of the BSPMs stored inmemory represent a pre-determined number of ECG signals; at least one ormore estimated BSPMs of the BSPMs stored in memory were calculated froma different number of ECG signals than the pre-determined number; and/orcalculation of the one or more estimated BSPMs comprises interpolatingbetween the different number of ECG signals by application ofcoefficients pre-derived from a collection of BSPMs each representingthe pre-determined number of ECG signals.

In another embodiment, there is provided a method for quantifying,during pacemapping, a comparison of a BSPM of interest to a pace siteBSPM, the method comprising: receiving at a computing device a pluralityof ECG signals from an acquisition system; calculating the pace siteBSPM using the plurality of ECG signals; comparing the BSPM of interestto the pace site BSPM, wherein comparing comprises: retrieving the BSPMof interest from memory accessible by the computing device; andcalculating one or more comparison metrics for the BSPM of interest ascompared to the pace site BSPM; and displaying on a user interface incommunication with the computing device an indication of similaritybetween the BSPM of interest and the pace site BSPM based on thecomparison metric calculated.

The present invention provides a system and method for localizingactivation sites of origin, such as VT exit site or site of origin. Thesite of origin may first be localized to a segment or segments of theheart by comparing a BSPM of interest, such as a BSPM calculated from aplurality of ECG signals recorded simultaneously during VT, to BSPMtemplates, one template for each pre-defined segment of the heart.

Each BSPM template may be derived from a library collection of BSPMscalculated from ECG measurements captured from many patients. The BSPMtemplate for a heart segment may be calculated by computing the mean oraverage of the corresponding library BSPMs associated with a pacing sitewithin the corresponding heart segment.

The library BSPMs of the collection may each be calculated directly froma pre-determined number of previously recorded ECG signals from apatient (“recorded BSPMs”). Where a patient has been measured using adifferent number of ECG signals, an “estimated BSPM” may be calculatedby interpolating between the different number of ECG signals recordedfrom the patient.

The interpolation may use coefficients, such as regression coefficients,pre-derived from a collection of recorded BSPMs to generate theestimated BSPM from integrals of ECG signals recorded from a differentnumber of ECG signals than the pre-determined number of ECG signals usedto generate the recorded BSPMs. In an aspect, the different number ofECG signals may comprise a smaller number of ECG signals than thepre-determined number.

In an aspect, the BSPM of interest may also be calculated from a smallernumber of ECG signals (i.e., a reduced set of ECG leads) by using eithergeneral or patient-specific regression coefficients. Localization of theactivation site of origin may be further facilitated during pacemappingby providing an objective measure of the similarity of the BSPM ofinterest to a pace site BSPM. The similarity between these BSPMs may bequantified by calculating waveform-comparison metrics.

As illustrated in FIG. 1, body surface potentials may be acquired usingan acquisition system 120 such as a BioSemi™ Mark-6. An array ofelectrodes 140_1 . . . 140_N (collectively 140) placed on the patient'storso may be connected via shielded cables or leads 110_1 . . . 110_N tothe acquisition system 120. The plurality of surface electrodes 140 may,for example, comprise one hundred and twenty disposable radiolucentAg/AgCl FoxMed™ surface electrodes. FIG. 2 illustrates one possiblearrangement of 120 leads on the torso for body surface potentialmapping. Electrocardiographic signals (ECG signals) are simultaneouslyrecorded using Wilson central terminal as reference. The acquisitionsystem 120 may transmit digitized signals to computing device 130, suchas a general purpose personal computer, for data display and recording.The signals may be transmitted through a fibre optic cable to thecomputing device 130. Data may be recorded to memory such as a harddrive of the computer. In one embodiment, the acquisition system 120samples each channel at at least 250 Hz and preferably at 2000 Hz, anddigitizes the ECG signals with at least 16-bit resolution. The analogECG signals may be amplified and filtered by an antialiasing bandpassfilter (e.g., 0.025 to 300 Hz). The raw data files may be recorded for15-30 seconds during abnormal rhythm, sinus rhythm or paced rhythm.Faulty signals (e.g., due to poor electrode-skin contact, motionartifacts, inaccessibility of the chest area where the electrodes weresupposed to be placed, etc.) may be rejected by the operator. ECGsignals recorded for BSPMs may include the standard 12 leads and/or theX, Y, Z leads.

The computing device 130 comprises a memory module and/or has access tomemory in which the recorded ECG signals, collection of BSPMs, set orlibrary of BSPM templates and/or coefficients for interpolation arestored. The computing device 130 also comprises and/or is incommunication with a user interface, such as a display.

For activation sites in the left ventricle of the heart, segments of theheart may be defined using electroanatomical maps of the left ventricle.For example, the left ventricle may be divided into 16 segments. Theleft ventricular cavity may be divided along its longitudinal axis intothree equal portions; basal, mid-cavity and apical. The basal and midportions can be divided into 6 segments. The circumferential segments inthe basal and mid-cavity may be anterior, anteroseptal, inferoseptal,inferior, inferolateral and anterolateral. Since the left ventricletapers as it approaches the apex, the apical segment can be divided to 4segments only: e.g. apical anterior, apical septal, apical inferior andapical lateral or alternatively, apical anteroseptal, apicalinferoseptal, apical anterolateral and apical inferolateral. This16-segment model is a modification from the 17-segment model ofCerqueira et al. [“Standardized myocardial segmentation and nomenclaturefor tomographic imaging of the heart”, Circulation 2002; 105:539-42](shown in FIG. 3) due to the absence of an endocardial representative ofsegment 17 (myocardial cap at the LV apex). The location of eachendocardial pacing site or VT origin/exit site may be projected visuallyfrom a CARTO™ map to its corresponding location on a polar projection ofthe left ventricle.

In one embodiment, recorded BSPMs are calculated by selecting a beatfrom stored recordings of ECG signals and calculating QRS time integralsfor each lead. The QRS time integral is calculated for each lead as thealgebraic sum of all potentials from the time instant of QRS onset toQRS offset multiplied by the sampling interval. In another embodiment,the time integral may be calculated for a specified duration, such asthe initial 100 msec of the QRS.

Estimated BSPMs may be calculated from ECG signals collected through adifferent number of leads than used for the recorded BSPMs. In anaspect, the different number corresponds to a reduced set of leads. Thecoefficients used for this calculation may be general regressioncoefficients derived from a collection or database of BSPMs or,alternatively, patient-specific coefficients derived from BSPMs recordedfor the same patient in a pre-procedure session. In another embodiment,data from a set of 32 leads may be used. The anatomical sites for these32 leads may be pragmatically selected based on accessibility of thesites, coverage of the standard 12-lead and X, Y and Z sites,configuration of available strips of electrodes, etc. Alternatively,sites for a given number of electrodes may be selected that arecalculated to be optimal using regression analysis on a collection ofBSPMs. As for the recorded BSPMs, the integrals may be calculated forQRS onset to QRS offset or for a specified duration, such as the initial100 msec of the QRS.

The use of estimated BSPMs greatly simplifies ECG data acquisitionduring ablation procedure, while maintaining high localization accuracyof BSPMs.

For patient-specific localization, a regression model may be used. As anexample, data from 12-lead ECG for the patient using a plurality ofpacing sites may be captured, with coordinates of the pacing site knownfrom a electroanatomical map, such as from CARTO™. The regressioncoefficients may then be determined using a least-squares-solution whenintegrals for each pacing site are determined. More specifically, usingparameters V₁ to V_(k) (where k=8 for the standard 12-lead ECG and k=3for vectorcardiogram (VCG)) to indicate the QRS-integrals derived fromthe patient's 12-lead ECG or 3-lead VCG for at least (k+1) pacing siteshaving coordinates X_(j), Y_(j) and Z_(j) for j=1 to k+1 obtain forexample from a CARTO™ system, the least-square regression can be used todetermine patient-specific coefficients, α_(i), β_(i) and γ_(i), for i=0to k to the equations for multiple regression with intercept asindicated in the following equations. At least k+1 pacing sites areneeded for a non-singular system to solve.X _(j)={circumflex over (α)}₀+Σ_(i=1) ^(k){circumflex over (α)}_(i) V_(i);  a.Y _(j)={circumflex over (β)}₀+Σ_(i=1) ^(k){circumflex over (β)}_(i) V_(i);  b.Z _(j)={circumflex over (γ)}₀+Σ_(i=1) ^(k){circumflex over (γ)}_(i) V_(i)  c.

In an aspect, two leads, termed discriminator leads, may be selected asinitial discriminators. The QRS integrals for each of the discriminatorleads may be identified as being either positive or negative. For twodiscriminator leads, this leads to four possible conditions or states:positive-positive; positive-negative; negative-positive; andnegative-negative. For each condition, 16 templates may be generated,one for each of the 16 segments, resulting in 64 templates in total. Theuse of these condition-specific templates improves the accuracy of thematching as the matching is done on a smaller set of templates.

The 16 templates associated with each of the conditions are preparedusing averaging integrals from pacing sites within that segment thathave the associated states for the discriminator leads.

The states of the discriminator leads, defined as one of the fourpossible conditions, may then be matched to the BSPM of interest and theBSPM of interest can be matched to the BSPM templates for that state.The BSPM of interest may be a full set of body surface potential mappingdate, such as 120 ECG signals, or a smaller set of ECG signals,typically from either 12 or more leads.

With reference to FIG. 12, panels A, B, C and D each show a set of 12lead ECG templates for 16 endocardial segments and a particular choiceof discriminator leads. Each panel is associated with one of the fourpossible conditions. For example, Part A depicts 12-lead ECG templatesobtained selectively from those patterns that feature a positive QRSintegral in both leads II and V4. The templates indicated under “Apical”show null or minimal values which indicates that if both leads II and V4are positive in a BSPM of interest, there is a low probability that thepacing site is in the apical or apex of the heart.

In an aspect, leads II and V4 may be used as discriminator leads. Eachcombination of 12-lead ECG pairs was analyzed to identify optimallocalization accuracy. Using leads II and V4 as discriminator leads, theinventors obtained a superior improvement in the mean accuracy foridentification of first, first/second and first/second/third segments.With reference to FIG. 12, 12-lead ECG QRS integral templates are shownfor positive and negative conditions of leads II and V4.

In an aspect, leads I and aVF may be used as discriminator leads. Thepositive or negative values of these leads can be used to determine thequadrant in the frontal plane of the mean electric axis of the QRScomplex.

BSPM templates or patterns for each segment are calculated by averagingthe integrals of all pacing sites within that segment, i.e. averagingall BSPMs for that segment, to obtain a mean BSPM map of the selectedinterval for each segment. Examples of 120-lead BSPM templates based onrecorded BSPMs are shown in FIGS. 8 and 9, FIG. 8 for the interval fromQRS onset to QRS offset and FIG. 9 for the initial 100 msec of the QRS.Examples of 120-lead BSPM templates based entirely on BSPMs estimatedfrom the 12-lead ECG by using general regression coefficients are shownin FIGS. 10 and 11, FIG. 10 for the interval from QRS onset to QRSoffset and FIG. 11 for the initial 100 msec of the QRS. In the examplesof FIGS. 8 to 11, isointegral lines or contours in areas 410 and 400represent positive and negative integrals, respectively. These areas 410and 400 are separated by a white line or contour 420 representing thezero integral. In an alternative embodiment, BSPM templates may be basedon a combination of recorded BSPMs and estimated BSPMs.

FIGS. 6 and 7 illustrate 12-lead ECG templates calculated in a similarmanner. Although ECG templates for fewer leads such as these may be usedto localize activation sites of origin to a segment, localizations maybe less accurate than systems that use higher lead count BSPMs.

As illustrated in FIG. 4, to localize a site of origin to a segment orgroup of segments, a plurality of ECG signals are simultaneous recordedat step 200 and a BSPM of interest is calculated at step 210. If thenumber of ECG signals simultaneously recorded matches the lead count ofthe BSPM templates, then the BSPM of interest is calculated as describedabove for recorded BSPMs. If the number of ECG signals simultaneouslyrecorded has a different lead count, for instance is a lower lead count,from the BSPM templates, then the BSPM of interest is calculated asdescribed above for estimated BSPMs. The BSPM of interest is thencompared to each BSPM template of a set or library of BSPM templates atstep 220. The set of BSPM templates comprises one template for eachpre-defined segment of the heart. To quantify the comparison of the BSPMof interest to each of the BSPM templates, the computing device 130retrieves the BSPM templates stored in memory and at least one waveformcomparison metric is calculated for each BSPM template as compared tothe BSPM of interest. Examples of waveform comparison metrics that maybe used include correlation coefficients, mean absolute deviation androot mean square of the difference. The BSPM template that most closelyresembles the BSPM of interest is identified at step 230 using thecalculated comparison metrics, e.g., BSPM template associated with thehighest correlation coefficient, the lowest mean absolute deviationand/or the lowest root mean square of the difference. As each BSPMtemplate is associated with a particular segment, the site of origin ofthe BSPM of interest can be localized if not to the identified segmentthen to the identified segment and segments adjacent to or near thelocalized segment.

By localizing the site of origin to a region of the heart, the area tobe mapped by the operator is limited, thereby reducing the time spent inperforming catheter mapping and ablation.

As illustrated in FIG. 5, localization of the activation site of originmay be further facilitated during pacemapping by providing an objectivemeasure of the similarity of a pre-recorded BSPM of interest to a pacesite BSPM in real-time. In one embodiment, the computing device 130receives a plurality of simultaneously recorded ECG signals at step 300associated with a pace site. The computing device 130 then calculates apace site BSPM at step 310. If the number of ECG signals receivedmatches the lead count of the BSPM of interest, then the pace site BSPMis calculated as described above for recorded BSPMs. If the number ofECG signals received is a different lead count, for instance is a lowerlead count, from the BSPM of interest, then the pace site BSPM iscalculated as described above for estimated BSPMs. The pace site BSPM isthen compared to the BSPM of interest at step 320. To quantify thesimilarity of the pace site BSPM to the BSPM of interest, the computingdevice 130 retrieves the BSPM of interest stored in memory and at leastone waveform comparison metric is calculated. Examples of waveformcomparison metrics that may be calculated include correlationcoefficients, mean absolute deviation and root mean square of thedifference. An indication of the similarity between the BSPM of interestand the pace site BSPM based on the calculated waveform metric(s) isthen displayed on the user interface, such as a computer monitor orscreen, in communication with the computing device at step 330.

In an embodiment, a high-resolution localization may be determined bymeans of regression analysis. With the origin of the coordinate systemat the center of the left ventricular (LV) cavity, a linear regressionwith intercept model may be applied to the k independent QRS-integralparameters derived from a 12-lead ECG or the VCG. The independent QRSintegral parameters are collected for a patient population along withthe location (X, Y and Z) of the pacing site determined using anelectroanatomical map, such as from CARTO™, and projected on theprototype left ventricle, such as depicted in FIG. 13. This data may bestored in a database for ease of access and analysis.

Using a least-squares solution to the prototype model of the heart whenapplied to the QRS-integral parameters obtained from the patient datasetmay provide general (i.e. for the population) regression coefficientsα_(i), β_(i) and γ_(i), for i=0, 1, . . . , k.

These general regression coefficients relate the three-dimensional leftventricle (LV) geometry to ECG or VCG parameters. By using thesecoefficients, the location of the pacing site on the endocardial surfacecan be estimated and as confirmation the estimated location can becompared to the actual location as determined by the electroanatomicalmap to determine a location error distance from the actual pacing sitein terms of distance on the endocardial surface. The system is trainedusing patient data where there is a known pacing site.

A three-dimensional prototype model of the left ventricle modelled froma human heart may be represented using planar triangles and divided intoanatomical segments. In an embodiment illustrated in FIG. 13,left-ventricular endocardial surface is tessellated into 238 planartriangles within 16 anatomical segments and projected onto a polardisplay. There are 6 basal segments (1-6), 6 mid-segments (7-12), and 4apical segments denoted as anterior (1 and 7), anteroseptal (2, 8, and13), inferoseptal (3, 9, and 14), inferior (4 and 10), inferolateral (5,11, and 15), and anterolateral (6, 12, and 16).

Using data from 26 patients, pacing data from 589 sites were analyzedfor training of the system and to test the accuracy of the regressionanalysis. The geodesic distance on the endocardial surface from thepredicted site to the actual pacing site was 13.07±9.88 mm with a medianvalue of 10.67 mm.

Using the general regression coefficients, ECG or VCG data may becollected and applied to determine a predicted location of the pacingsite. This predicted location may be used to help converge on the likelylocation of the pacing site for either manual exploration or for usewith electroanatomical mapping. In an embodiment, a computing devicesuch as a tablet or portable computer may be used to apply the generalregression coefficients to the received ECG or VCG data from a patientto determine the predicted location of the pacing site. The top-rightportion of FIG. 14 shows a display of a representation of the LVindicating the likely location as could be displayed on a display screenof the tablet or portable computer. A user, such as a clinicalelectrophysiologist at the patient, may view the display screen with theindicated position. As would be understood, the analysis and/or displaymay be done away from the patient, such as at a server or othercomputing system connected to the ECG/VCG data acquisition system overan electronic network. The data and likely location could be saved fordisplay or analysis at a later time or displayed in real time to a user,such as a doctor or electrophysiologist, at the patient.

With reference to the top-left portion of FIG. 14, the location ofpredicted pacing site may be indicated with reference to the 16 segmentsof the left ventricle. The predicted location may be indicated using thecorrelation coefficients, produced by template matching, displayed inpercent for each segment. The best fit of the triangularelements—obtained by means of the regression method—may be highlighted,such as indicated by the white triangle in FIG. 14.

The top-right portion of FIG. 14 indicates the predicted site on a 3Dmodel of the left ventricle, again with probabilities for each segment.

With reference to the bottom left portion of FIG. 14, the initial 120 msof the QRS is indicated. In this case, tracings are indicated for 8frontal-plane leads and 6 precordial leads of the standard 12-lead ECG,although a different set of leads may be used. The location of thepacing site derived from the electroanatomic map, such as from theCARTO™ system, shown in the bottom right of FIG. 14 is for comparisonand verification of the predicted location.

From various embodiments of the present invention having been thusdescribed in detail by way of example, it will be apparent to thoseskilled in the art that variations and modifications may be made withoutdeparting from the invention. The invention includes all such variationsand modifications as fall within the scope of the appended claims.

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We claim:
 1. A method for localizing an activation site of origin to asegment of the heart comprising: obtaining a body-surface potential map(BSPM) of interest based on a plurality of simultaneously recordedelectrocardiographic (ECG) signals from a first plurality of at leastthree leads; calculating an integral of the signal recorded from asecond plurality of leads selected from the first plurality of leadswherein the second plurality of leads comprises between two leads andone less than the first plurality of leads and identifying a conditionof the integral as positive or negative for each of the second pluralityof leads; comparing the identified condition of each of the secondplurality of leads with a condition of a plurality of pre-determinedsets of BSPM templates, each set comprising one BSPM template for eachpre-defined segment of the heart, to identify a selected set of BSPMtemplates having matching conditions; comparing the body surfacepotential map of interest to each BSPM template of the selected set ofBSPM templates, wherein comparing comprises, for each BSPM template ofthe set of BSPM templates: retrieving a BSPM template from memory fromthe selected set of BSPM templates; and calculating one or morecomparison metrics for the BSPM template retrieved as compared to theBSPM of interest, wherein each of the BSPM templates was generated byaveraging BSPMs of all pacing sites within the associated segment of theheart from a collection of BSPMs stored in memory; and identifying theBSPM template that most closely resembles the BSPM of interest using theone or more comparison metrics calculated in order to identify thesegment of the heart in which the activation site of origin is likely tobe located.
 2. The method of claim 1 wherein each of the BSPM templatesof the set of BSPM templates represents a pre-determined number of ECGsignals.
 3. The method of claim 2 wherein the BSPMs of interest werecalculated from a different number of ECG signals than thepre-determined number.
 4. The method of claim 3 wherein calculation ofthe BSPM of interest comprises interpolating between the differentnumber of ECG signals by application of either general orpatient-specific coefficients pre-derived from a collection of BSPMseach representing the pre-determined number of ECG signals.
 5. Themethod of claim 1 wherein the second plurality of leads comprises leadsI and a VF of a 12-lead ECG.
 6. The method of claim 1 wherein the secondplurality of leads comprises leads II and VF of a 12-lead ECG.
 7. Themethod of claim 4 wherein pre-deriving said coefficients comprisesstimulating a plurality of known endocardial sites in order to producetachycardia while capturing data representative of the tachycardia usinga plurality of leads and subsequently applying a least-squaresregression against a location of each of the known endocardial sites andthe captured data.
 8. The method of claim 7 wherein the plurality ofknown endocardial sites are on a heart of one patient.
 9. The method ofclaim 7 wherein the plurality of known endocardial sites are on aplurality of different hearts.
 10. A method for localizing an activationsite of origin to an area of the heart comprising: retrieving aplurality of general coefficient parameters relating a physical locationof an activation site to integrals of ECG signals obtained by i)obtaining a plurality of simultaneously recorded electrocardiographic(ECG) signals from a plurality of leads, from each of a plurality ofpatients; ii) calculating an integral of the signal recorded from two ofthe plurality of leads; iii) obtaining the location of the activationsite for each of the plurality of patients; and iv) applying a linearregression with intercept to the integrals and to the location of thepacing site to obtain the plurality of general coefficient parameters;obtaining a plurality of simultaneously recorded electrocardiographic(ECG) signals of interest from the plurality of leads used to obtain thegeneral coefficient parameters, from a patient; calculating an integralof the ECG signal recorded from each of the plurality of leads for thepatient; applying the general coefficient parameters to the integralsfor the patient to obtain an estimated activation site; and displayingthe location of the estimated activation site on a model of a heart.